Apache Bigtop
The comprehensive infrastructure for packaging, testing, and configuration of the Big Data ecosystem.
Cube is the industry-leading universal semantic layer designed to bridge the gap between complex data sources and downstream applications, including BI tools, customer-facing dashboards, and Large Language Models (LLMs). Technically, Cube centralizes data modeling, access control, and caching into a unified middleware layer. Its core architecture consists of the Cube Store—a purpose-built, high-performance columnar storage engine—and a multi-protocol API (SQL, REST, GraphQL) that ensures consistency across all consumers. In the 2026 landscape, Cube has pivoted heavily toward 'Semantic Layer for AI,' providing the deterministic context necessary for LLMs to query structured data without hallucinations. By defining metrics, dimensions, and joins in a code-first environment (YAML or JavaScript), organizations can ensure that an AI agent and a human-led dashboard retrieve the exact same revenue or churn figures. Cube’s ability to handle complex pre-aggregations and multi-tenant security at scale makes it the standard for enterprises building RAG-based data agents and real-time analytical products. It supports all major cloud data warehouses including Snowflake, BigQuery, Databricks, and Athena.
A purpose-built columnar storage layer for caching and pre-aggregations, using Rust and Arrow.
The comprehensive infrastructure for packaging, testing, and configuration of the Big Data ecosystem.
The language-agnostic standard for high-performance in-memory columnar data processing and zero-copy serialization.
AI-Powered Database Observability and Autonomous Query Optimization
The enterprise-grade natural language interface for structured data.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Exposes metadata and schema definitions as context for LLMs to generate accurate SQL.
Provides SQL, REST, and GraphQL endpoints simultaneously for the same data model.
Automatic background refreshing of data summaries based on defined intervals or triggers.
Allows generating data models dynamically based on the security context or tenant ID of the user.
Version control for data models with support for branching and PR-based deployments.
Row-level and column-level security defined at the semantic level.
LLMs often generate incorrect SQL or invent metrics when querying raw data directly.
Registry Updated:2/7/2026
Results are returned with high accuracy.
Slow performance and high costs when querying Snowflake/BigQuery for thousands of end-users.
Different numbers for 'Revenue' in Tableau vs. PowerBI due to inconsistent SQL logic.